Removal of Cyclic Borehole Noise From Low- and High-Resolution LWD Images and Its Impact on Image Interpretation
- Junichi Sugiura (Schlumberger) | Rick Lee (PathFinder, a Schlumberger company)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- August 2012
- Document Type
- Journal Paper
- 462 - 472
- 2012. Society of Petroleum Engineers
- 3.3.2 Borehole Imaging and Wellbore Seismic, 1.6 Drilling Operations, 5.6.1 Open hole/cased hole log analysis, 1.12.2 Logging While Drilling
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- 577 since 2007
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The use of logging-while-drilling (LWD) imaging tools in real-time decision making and post-drilling analysis has become commonplace. However, image noise and processing errors because of inherent measurement physics can propagate errors and, thus, complicate interpretation of openhole log data and images. For example, standard density images tend to amplify image noise from small borehole irregularities. Among different borehole irregularities, spiraling is known to occur more frequently with conventional rotary assemblies, steerable motor assemblies, and rotary-steerable assemblies. When the cyclic-noise amplitude from spiraling becomes large relative to the measurement of the primary interest, it grossly affects the quality of the recorded formation bulk density, photoelectric factor, and neutron porosity.
This paper shows novel methods to remove cyclic noise from formation-evaluation (FE) images by applying frequency-domain filtering. Although the initial attempt of fast Fourier transforms (Zhang et al. 2010) illustrates the straightforward concept, it is seldom used because of implementation issues requiring interactive filter design and intensive operator intervention. Recently, a new method (Sugiura et al. 2011) has been designed to improve the filtering process. Additionally, the new adaptive filter designs automate the cyclic-noise-removal process from the FE images. Standalone software has been developed to process the entire image logs from one well, without human supervision. The software adaptively modifies the filter behavior as borehole-oscillation noise characteristics change with formation, drilling assemblies, hole inclination, and depth.
To examine its validity qualitatively, the algorithm is then applied to various field data, not only on low-resolution density images but also on higher-resolution density images. The new algorithm has been proved by bringing considerable improvement to the image quality without any artificial interruptions. The rugosity effect is reduced significantly, and the apparent resolution of bed-boundary features is increased.
Furthermore, comparative field examples illustrate the improvement in image-feature extraction. The method described here is critical, for example, for small-fracture detection and more-precise definition of formation-property contrasts indicative of bed boundaries in irregular boreholes. This new method not only is effectively used for density images but also is applicable to any other borehole images, such as resistivity and ultrasonic images.
|File Size||18 MB||Number of Pages||11|
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